A case study: how Altitude Group built GTM-OS — an autonomous go-to-market operator — in 21 days. It runs the full GTM workflow without a hire, and it already patched its own production bug overnight.
< 30 min
founder time to live GTM
13.8 sec
5-email sequence written
97.8%
agent run success rate
21 days
from zero to live
A cron job ran at 7am UTC. It queried the live database, read the task outputs, and found two problems: an autopilot credit-check bug and a task queue that was replenishing itself out of control.
It wrote the fixes. It ran the health check. It deployed to production.
No engineer opened a ticket. No one approved a pull request. The system that runs go-to-market for founders had just run maintenance on itself.
That system is GTM-OS. We built it in a 21 day sprint. This is how, and what it proves about agentic products.
Seed-stage founders have to run GTM themselves, by hand, with no system and no time. The alternative is a $150K first GTM hire they cannot yet justify.
GTM-OS removes that choice. Paste a URL. The platform analyses the site, surfaces ICP hypotheses, generates a task queue, and the agents start working. ICP definition, competitor research, landing pages, cold email sequences, prospect lists, social posts. Each output runs against shared project memory, so every output knows what every other output did.
Nothing ships without a founder approving it first.
The point is not that it does GTM faster. The point is that the work runs without the founder present.
The system does the work in the background. The human holds the gate. Everything else is speed.
The platform is days old. Volume numbers are deliberately small. The point is the speed of the work and the fact that it runs itself.
< 30 min
Founder time to full GTM setup
36 sec
Average task execution
13.8 sec
5-email sequence written
2–3 hrs
Same sequence, human time
97.8%
Agent run success rate
1
Autonomous prod fixes shipped
21 days
Build time
Live, daily
Status
Most software waits for a human to find what is broken. GTM-OS looks for it on a timer.
A Self-Improvement Agent runs daily at 7am UTC. It queries the live database, reads every task output and error, and identifies the top three issues. It attempts the code fixes itself, deploys only after a health check passes, and escalates anything it cannot solve to a Telegram alert.
On one of those runs it caught two real problems — an autopilot credit-check bug, and a task replenishment loop generating work faster than it should. It patched both and shipped them in a single cron run.
This is not a chatbot answering questions. It is an operator that reads its own performance and acts on it. The feedback loop that founders never close by hand, the system closes on itself every morning.
Every agent run produces typed outputs that are validated before they are saved. Landing page HTML, email sequence markdown, contact JSON. Models run on Claude Sonnet 4 via AWS Bedrock. No finetuning. The prompts carry all the context.
Fetches the URL, analyses the site in ~36 seconds, generates ICP hypotheses, and produces the first task queue.
Looks at what is done, what is queued, and what is missing — then generates the next batch of work.
Picks the next ready task and routes it to the right workflow: landing page, email sequence, social post, contact list. Executes with full project context.
Reads the live database, finds the top three issues, writes the code fix, runs the health check, deploys to production. Escalates anything it cannot solve.
An autonomous system that touches outbound and ships its own code needs hard limits. GTM-OS has them in the data model, not as suggestions.
Nothing sends without explicit approval. An approval record moves from pending to approved before any landing page goes live or any email leaves the building. Social posts are draft only.
Code changes from the Self-Improvement Agent are committed to git and only deployed after a health check passes. If it fails, the change does not ship.
A credit system caps spend per task and blocks work at zero. A replenishment guard stops the queue from flooding itself. Every run is logged with tokens, duration, workflow.
One sprint. A working, self-improving, agentic product. Not a prototype. Production.
The agents, the project memory model, the task queue, the approval gates, the data model. Every decision made before code, so the build had no open questions.
Each agent wired to its workflow. Structured outputs validated. The approval records table standing between every agent and anything customer-facing.
The cron that analyses and fixes the system itself landed last. By the end of the sprint, GTM-OS was live, running tasks for real users, maintaining itself daily.
A complex agentic system — one that runs a full go-to-market workflow and patches its own code — was built and shipped in 21 days.
Defining the agents, drawing the approval gates, validating the outputs, and shipping it as a real product instead of a demo. That is what a sprint forces.
Most teams sitting on an AI idea have the same gap. The models exist. The use case is clear. What is missing is the discipline to turn it into something live that runs every day without a babysitter. That is a solvable problem. The sprint is how it gets solved.
Altitude Group takes defined business problems and turns them into working agentic products in 21 days. Fully project managed. Fixed scope. Fixed price. Three disciplines in one delivery: product management, project management, and AI-native execution.
We pressure test whether your problem fits a sprint, not a salary.
The deal
From $30,000.
Fixed price.
21 days.
A live product.
GTM-OS is the proof case. An autonomous GTM operator that runs the work of a $150K hire, ships its own fixes to production, and was built in three weeks.
All platform figures sourced from the live GTM-OS production database, accurate as of June 2026. GTM-OS is days old at the time of writing. Volume numbers are deliberately small and honest. The claims in this post are about speed, autonomy, and the system's ability to improve itself — all of which are verifiable in production.